Multiple output relaxation machine learning model

ABSTRACT

A multiple output relaxation (MOR) machine learning model. In one example embodiment, a method for employing an MOR machine learning model to predict multiple interdependent output components of a multiple output dependency (MOD) output decision may include training a classifier for each of multiple interdependent output components of an MOD output decision to predict the component based on an input and based on all of the other components. The method may also include initializing each possible value for each of the components to a predetermined output value. The method may further include running relaxation iterations on each of the classifiers to update the output value of each possible value for each of the components until a relaxation state reaches an equilibrium or a maximum number of relaxation iterations is reached. The method may also include retrieving an optimal component from each of the classifiers.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of International Patent ApplicationNo. PCT/US13/55859, filed on Aug. 20, 2013, which is a continuation ofU.S. patent application Ser. No. 13/590,028, filed Aug. 20, 2012, eachof which is incorporated herein by reference in its entirety.

FIELD

The embodiments discussed herein are related to a multiple outputrelaxation (MOR) machine learning model.

BACKGROUND

Machine learning is a form of artificial intelligence that is employedto allow computers to evolve behaviors based on empirical data. Machinelearning may take advantage of training examples to capturecharacteristics of interest of their unknown underlying probabilitydistribution. Training data may be seen as examples that illustraterelations between observed variables. A major focus of machine learningresearch is to automatically learn to recognize complex patterns andmake intelligent decisions based on data.

One main difficulty in machine learning lies in the fact that the set ofall possible behaviors, given all possible inputs, is too large to becovered by a set of training data. Hence, a machine learning model mustgeneralize from the training data so as to be able to produce a usefuloutput in new cases.

One example of machine learning is traditional structured prediction(SP). Traditional SP is a single model approach to dependent output.With SP, once an input feature vector x is specified, a single correctoutput vector z can be fully specified. Thus the output vector z isfully conditioned on the input feature vector x and the different outputcomponents of output vector z (z₁, z₂, . . . ) are conditionallyindependent of each other given the input feature vector x. Thus, theprobability of z₁ given x is equal to the probability of z₁ given x andz₂, or p(z₁|x)=p(z₁|x, z₂). However, traditional SP cannot handle aninterdependent relationship between different output components. Inaddition, traditional SP cannot handle a problem having multiple correctoutput decisions for a given input.

The subject matter claimed herein is not limited to embodiments thatsolve any disadvantages or that operate only in environments such asthose described above. Rather, this background is only provided toillustrate one example technology area where some embodiments describedherein may be practiced.

SUMMARY

In general, example embodiments described herein relate to methods ofemploying a multiple output relaxation (MOR) machine learning model topredict multiple interdependent output components of a multiple outputdependency (MOD) output decision. The example methods disclosed hereinmay be employed to solve MOD problems.

In one example embodiment, a method for employing an MOR machinelearning model to predict multiple interdependent output components ofan MOD output decision may include training a classifier for each ofmultiple interdependent output components of an MOD output decision topredict the component based on an input and based on all of the othercomponents. The method may also include initializing each possible valuefor each of the components to a predetermined output value. The methodmay further include running relaxation iterations on each of theclassifiers to update the output value of each possible value for eachof the components until a relaxation state reaches an equilibrium or amaximum number of relaxation iterations is reached. The method may alsoinclude retrieving an optimal component from each of the classifiers.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory and arenot restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

Example embodiments will be described and explained with additionalspecificity and detail through the use of the accompanying drawings inwhich:

FIG. 1 is a schematic block diagram illustrating an example leadresponse management (LRM) system including an example contact server;

FIG. 2 is a schematic block diagram illustrating additional details ofthe example contact server of FIG. 1;

FIG. 3A is a schematic flow chart diagram illustrating an examplemultiple output relaxation (MOR) machine learning model;

FIG. 3B is a text diagram illustrating an example input feature vector;

FIG. 3C is a schematic flow chart diagram illustrating a first examplemultilayer perceptron (MLP) neural network that is employed to predict afirst interdependent output component;

FIG. 3D is a schematic flow chart diagram illustrating a second exampleMLP neural network that is employed to predict a second interdependentoutput component;

FIG. 4 is a schematic flow chart diagram of an example method ofemploying an MOR machine learning model to predict multipleinterdependent output components of a multiple output dependency (MOD)output decision;

FIG. 5 is a schematic flow chart diagram of multiple correct MOD outputdecisions;

FIG. 6 illustrates an example computer screen image of a user interfaceof an example customer relationship management (CRM) system;

FIG. 7 illustrates an example computer screen image of a user interfaceof an example LRM system;

FIG. 8A illustrates an example computer screen image of an example leadadvisor display before a lead has been selected by an agent; and

FIG. 8B illustrates an example computer screen image of the example leadadvisor display of FIG. 8A after a lead has been selected by an agent.

DESCRIPTION OF EMBODIMENTS

Some embodiments described herein include methods of employing amultiple output relaxation (MOR) machine learning model to predictmultiple interdependent output components of a multiple outputdependency (MOD) output decision. The example methods disclosed hereinmay be employed to solve MOD problems.

As used herein, the term “multiple output dependency” or “MOD” refers toan output decision, or a problem having an output decision, thatincludes multiple output components which are interdependent in thateach component is dependent not only on an input but also on the othercomponents. Some example MOD problems include, but are not limitedto: 1) which combination of stocks to purchase to balance a mutual fundgiven current stock market conditions, 2) which combination of playersto substitute into a lineup of a sports team given the current lineup ofthe opposing team, and 3) which combination of shirt, pants, belt, andshoes to wear given the current weather conditions. In each of theseexamples, each component of the output decision depends on both theinput (current stock market conditions, an opposing team lineup, orcurrent weather conditions) and the other components (the other stockspurchased, the other substituted player, or the other clothingselected). Other examples of MOD problems may relate to hostagenegotiations, retail sales, online shopping carts, web contentmanagement systems, customer service, contract negotiations, or crisismanagement, or any other situation that requires an output decision withmultiple interdependent output components.

Another example MOD problem is lead response management (LRM). LRM isthe process of responding to leads in a manner that optimizes contact orqualification rates. Leads may come from a variety of sources including,but not limited to, a web form, a referral, and a list purchased from alead vendor. When a lead comes into an organization, the output decisionof how to respond to the lead may include multiple interdependentcomponents such as, but not limited to, who should respond to the lead,what method should be employed to respond to the lead, what contentshould be included in the response message, and when should the responsetake place. Each of these components of the output decision depends onboth the input (the lead information) and the other components. Forexample, the timing of the response may depend on the availability ofthe person selected to respond. Also, the content of the message maydepend on the method of response (e.g. since the length of an emailmessage is not limited like the length of a text message). Although theexample methods disclosed herein are generally explained in the contextof LRM, it is understood that the example methods disclosed herein maybe employed to solve any MOD problem.

Example embodiments will be explained with reference to the accompanyingdrawings.

FIG. 1 is a schematic block diagram illustrating an example LRM system100. As depicted, the example LRM system 100 includes various componentssuch as a public switched telephone network (PSTN) 110, usercommunication and/or computing devices 112, a TDM gateway 120 connectingthe PSTN 100 to an internet 130, remote agent stations 121, workstations128, a call center 140, an internet gateway 150 connecting a local areanetwork 160 to the internet 130, a web server 170, a contact server 200,a lead data server 190, local agent workstations 192, and controlworkstations 194. The various components of the example LRM system 100operably interconnected to collaboratively improve a process ofresponding to leads in a manner that optimizes contact or qualificationrates.

As disclosed in FIG. 1, the remote agent stations 121 include wirelessphones 122, wired phones 124, wireless computing devices 126, andworkstations 128. In certain embodiments, the wireless phones 122 or thewired phones 124 may be voice over internet protocol (VOIP) phones. Insome embodiments, the computing devices 126 or the workstations 128 maybe equipped with a soft phone. The remote agent stations 121 enableagents to respond to lead from remote locations similar to agentsstationed at the workstations 192 and directly connected to the localarea network 160.

In one example embodiment, the local area network 160 resides within acall center 140 that uses VoIP and other messaging services to contactusers connected to the PSTN 110 and/or the internet 130. The variousservers in the call center 140 function cooperatively to acquire leads,store lead information, analyze lead information to decide how best torespond to each lead, distribute leads to agents via agent terminalssuch as the local agent workstations 192 and the remote agent stations121 for example, facilitate communication between agents and leads viathe PSTN 110 or the internet 130 for example, track attempted andsuccessful agent interaction with leads, and store updated leadinformation.

The web server 170 may provide one or more web forms 172 to users viabrowser displayable web pages. The web forms may be displayed to theusers via a variety of communication and/or computing devices 112including phones, smart phones, tablet computers, laptop computers,desktop computers, media players, and the like that are equipped with abrowser. The web forms 172 may prompt the user for contact data such asname, title, industry, company information, address, phone number, faxnumber, email address, instant messaging address, referral information,availability information, and interest information. The web server 170may receive the lead information associated with the user in response tothe user submitting the web form and provide the lead information tocontact server 200 and the lead data server 190, for example.

The contact server 200 and the lead data server 190 may receive the leadinformation and retrieve additional data associated with the associateduser such as web analytics data, reverse lookup data, credit check data,web site data, web site rank information, do-not-call registry data,data from a customer relationship management (CRM) database, andbackground check information. The lead data server 190 may store thecollected data in a lead profile (not shown) and associate the user withan LRM plan (not shown).

The contact server 200 may contact a lead in accordance with anassociated LRM plan and deliver lead information to an agent to enablethe agent to respond to the lead in a manner that optimizes contact orqualification rates. The particular purpose of such contact orqualification may include, for example, establishing a relationship withthe lead, thanking the lead for their interest in a product, answeringquestions from the lead, informing the lead of a product or serviceoffering, selling a product or service, surveying the lead on theirneeds and preferences, and providing support to the lead. The contactserver 200 may deliver the information to the agent using a variety ofdelivery services such as email services, instant messaging services,short message services, enhanced messaging services, text messagingservices, telephony-based text-to-speech services, and multimediadelivery services. The agent terminals 121 or 192 may present the leadinformation to the agent and enable the agent to respond to the lead bycommunicating with the lead.

FIG. 2 is a schematic block diagram illustrating additional details ofthe example contact server 200 of FIG. 1. As disclosed in FIG. 2, thecontact server 200 includes a contact manager 210, a dialing module 220,a messaging module 230, a PBX module 240 and termination hardware 250.In the depicted embodiment, the contact manager includes an MOR machinelearning module 212, an LRM plan selection module 214, an agentselection module 216, and a lead data server access module 218. Althoughshown within the contact server 200, the depicted modules may residepartially or wholly on other servers such as the web server 170 and thelead data server 190 for example. The contact server 200 enables anagent to communicate with a lead in conjunction with an LRM plan.

The contact manager 210 establishes contact with users and agents andmanages contact sessions where needed. The contact manager 210 mayinitiate contact via the dialing module 220 and/or the messaging module230.

The MOR machine learning module 212 employs an MOR machine learningmodel to predict multiple interdependent output components of an MODoutput decision, according to the example methods disclosed herein. Inat least some example embodiments, the MOR machine learning module 212utilizes the lead data server access module 208 to access and analyzelead information stored on the lead data server 190 of FIG. 1. Once oneor more response decisions are predicted for a particular lead, the oneor more response decisions may be conveyed to the LRM plan selectionmodule 214.

The LRM plan selection module 214 presents and or selects one or moreLRM plans for a particular lead and/or offering. Similarly, the agentselection module 216 selects an agent, class of agent, or agent skillset that is designated in each LRM plan.

The lead data server access module 218 enables the contact manager 210to access lead information that is useful for contacting a lead. In oneembodiment, the data storage access module 218 enables the contactmanager 210 to access the lead data server 190.

The dialing module 220 establishes telephone calls including VOIPtelephone calls and PSTN calls. In one embodiment, the dialing module220 receives a unique call identifier, establishes a telephone call, andnotifies the contact manager 210 that the call has been established.Various embodiments of the dialing module 220 incorporate auxiliaryfunctions such as retrieving telephone numbers from a database,comparing telephone numbers against a restricted calling list,transferring a call, conferencing a call, monitoring a call, playingrecorded messages, detecting answering machines, recording voicemessages, and providing interactive voice response (IVR) capabilities.In some instances, the dialing module 220 directs the PBX module 240 toperform the auxiliary functions.

The messaging module 230 sends and receives messages to agents andleads. To send and receive messages, the messaging module 230 mayleverage one or more delivery or messaging services such as emailservices, instant messaging services, short message services, textmessage services, and enhanced messaging services.

The PBX module 240 connects a private phone network to the PSTN 110. Thecontact manager 210 or dialing module 220 may direct the PBX module 240to connect a line on the private phone network with a number on the PSTN110 or internet 130. In some embodiments, the PBX module 240 providessome of the auxiliary functions invoked by the dialing module 220.

The termination hardware 250 routes calls from a local network to thePSTN 110. In one embodiment, the termination hardware 250 interfaces toconventional phone terminals. In some embodiments and instances, thetermination hardware 250 provides some of the auxiliary functionsinvoked by the dialing module 220.

Having described a specific environment (an LRM system) and specificapplication (LRM) with respect to FIGS. 1 and 2, it is understood thatthis specific environment and application is only one of countlessenvironments and application in which example embodiments may beemployed. The scope of the example embodiments are not intended to belimited to any particular environment or application.

FIG. 3A is a schematic flow chart diagram illustrating an example MORmachine learning model 300. The model 300 is configured to be employedin sequential decision making to predict multiple interdependent outputcomponents, namely z₁, z₂, z₃, and z₄, of an MOD output decision z.Although the output decision z includes four (4) components, it isunderstood that an MOR machine learning model could be employed inconnection with any output decision having two (2) or moreinterdependent components. The model 300 may be trained based onrecorded historical data so that it can make optimal (or near-optimal)decisions, especially when a decision is comprised of many variablesthat need to be determined at the same time.

Although the model 300 may be employed in any number of applications toproduce MOD output decisions, the model 300 is employed in FIG. 3A toproduce an LRM MOD output decision. In particular, the model 300 isemployed to decide for a given lead what response should be performednext in a sequence that will optimize the contact or qualification ofthe lead.

For example, the model 300 may be employed to produce an LRM MOD outputdecision z=(z₁, z₂, z₃, z₄), where z₁, z₂, z₃, and z₄ are fourcomponents of the output decision z, based on an input x. In thisexample, z₁=response agent title, z₂=response method, z₃=responsemessage type, and z₄=response timing. The input x may be an inputfeature vector that includes information about a particular lead.

It is understood that the components of response agent title, responsemethod, response message type, and response timing are only examplecomponents of an LRM MOD output decision. Other example components mayinclude, but are not limited to, agent or lead demographic profile,agent or lead histographic profile (i.e. a profile of events in the lifeof the agent or the lead which could include past interactions betweenthe agent and the lead), lead contact title (i.e. the title of aparticular contact person within a lead organization), agent or leadpsychographic profile (i.e. a profile of the psychologicalcharacteristics of the agent or the lead), agent or lead social networkprofile (i.e. the proximity of the agent to the lead in an online socialnetwork such as LinkedIn® or FaceBook® or in an offline social networksuch as the Entrepreneurs Organization®, civic clubs, fraternities, orreligions), agent or lead geographic profile (i.e. cities, states, orother geographic designations that define current and/or past locationsof the agent or the lead), response frequency (i.e. how often an agentcontacts a lead), and response persistence (i.e. how long an agentpersists in contacting a lead).

FIG. 3B is a text diagram illustrating an example input feature vectorx. The example input feature vector x of FIG. 3B includes informationabout a particular lead. In particular, the example input feature vectorx includes constant features about a lead, such as lead title and leadindustry, and interactive features related to interactions between anagent and the lead, such as previous number of dials and previousaction. The lead information provided by the example input featurevector x may be employed as input by the model 300 of FIG. 3A in orderto determine what is the next sequential response that should beperformed that will optimize the contact or qualification of the lead.

It is understood that the input features of lead source, lead title,lead industry, lead state, lead created date, lead company size, leadstatus, number of previous dials, number of previous emails, previousaction, and hours since last action are only example input features toan LRM MOD output decision. Other example input features may include,but are not limited to, response agent title, response method, responsemessage type, response timing, agent or lead demographic profile, agentor lead histographic profile, agent or lead psychographic profile, agentor lead social network profile, agent or lead geographic profile,response frequency, and response persistence. Additionally, inputfeatures could include data on current events, such as current eventsrelated to politics, economics, natural phenomena, society, and culture.It is further understood that where a particular input feature isemployed as an input to a particular LRM MOD output decision, theparticular input feature will not be included among the outputcomponents of the particular LRM MOD output decision.

As disclosed in FIG. 3A, there is a dependency among components z₁, z₂,z₃, and z₄. For example, a decision on the component z₂ (responsemethod) may have an influence on the decision for the component z₄(response timing). For example, if z₂=dial, an agent may need toconsider when a lead is available to talk on a phone (e.g. usuallyduring business hours of the time zone where the lead resides). Ifz₂=email, the agent may send the email at any time.

Therefore, in the example application of FIG. 3A, and as is the casewith other MOD output decisions, the components of z are dependent bothon an input x and on the other components of z. Thus, in this example,the probability of z₁ given x is not necessarily equal to theprobability of z₁ given x and z₂, or p(z₁|x)≠p(z₁|x, z₂). In otherwords, it cannot be decided what value a specific component of z shouldtake on without considering x and the values of the other components ofz.

The model 300 of FIG. 3A employs a base classifier. In particular, andas disclosed in FIG. 3A, the model 300 employs multilayer perceptron(“MLP”) neural networks MLP1, MLP2, MLP3, and MLP4 as base classifiers.It is understood, however, that the model 300 could alternatively employother types of base classifiers including, but not limited to othermultilayer neural networks, decision trees, and support vector machines.

FIG. 3C is a schematic flow chart diagram illustrating the MLP neuralnetwork MLP1 that is employed to predict the first interdependent outputcomponent z₁ based on the input feature vector x of FIG. 3B and based onthe predicted second interdependent output component z₂ of FIG. 3D aswell as the predicted third and fourth interdependent output componentsz₃ and z₄. In FIG. 3C, the input feature vector x and the inputcomponents z₂, z₃, and z₄ are received by an input layer of the MLPneural network MLP1 and then processed by a hidden layer and an outputlayer to predict z₁ε{z₁₁, z₁₂, z₁₃}.

FIG. 3D is a schematic flow chart diagram illustrating the MLP neuralnetwork MLP2 that is employed to predict the second interdependentoutput component z₂ based on the input feature vector x of FIG. 3B andbased on the predicted first interdependent output component z₁ of FIG.3C as well as the predicted third and fourth interdependent outputcomponents z₃ and z₄. In FIG. 3D, the input feature vector x and theinput components z₁, z₃, and z₄ are received by an input layer of theMLP neural network MLP2 and then processed by a hidden layer and anoutput layer to predict z₂ε{z₂₁, z₂₂, z₂₃}. As disclosed in FIG. 3A,MLP3 and MLP4 function in a similar manner to MLP1 and MLP2.

FIG. 4 is a schematic flow chart diagram of an example method 400 ofemploying an MOR machine learning model to predict multipleinterdependent output components of an MOD output decision. The method400 may be implemented, in at least some embodiments, by the MOR machinelearning module 212 of the contact manager 210 of the contact server 210of FIG. 1. For example, the MOR machine learning module 212 may beconfigured to execute computer instructions to perform operations ofemploying the MOR machine learning model 300 of FIG. 3A to predictmultiple interdependent output components z₁, z₂, z₃, and z₄ of an LRMMOD output decision z, as represented by one or more of blocks 402, 404,406, 408, 410, and 412 of the method 400. Although illustrated asdiscrete blocks, various blocks may be divided into additional blocks,combined into fewer blocks, or eliminated, depending on the desiredimplementation. The method 400 will now be discussed with reference toFIGS. 1-4.

The method 400 may begin at block 402, in which a classifier for each ofmultiple interdependent output components of an output decision istrained to predict the component based on an input and based on all ofthe other components. For example, the MOR machine learning module 212may train the MLP neural networks MLP1, MLP2, MLP3, and MLP4 to predicteach of the components z₁, z₂, z₃, and z₄ based on the input featurevector x of FIG. 3B and based on all of the other predicted components.In the example embodiment disclosed in FIG. 3A, z₁=response agent title,z₂=response method, z₃=response message type, and z₄=response timing.Thus, MLP1 is trained from (x, z₂, z₃, z₄; z₁) to predict response agenttitle z₁ using x, z₂, z₃, and z₄ as input; MLP2 is trained from (x, z₁,z₃, z₄; z₂) to predict response method z₂ using x, z₁, z₃, and z₄ asinput; MLP3 is trained from (x, z₁, z₂, z₄; z₃) to predict responsemessage type z₃ using x, z₁, z₂, and z₄ as input; and MLP4 is trainedfrom (x, z₁, z₂, z₃; z₄) to predict response timing z₄ using x, z₁, z₂,and z₃ as input.

It is understood that since the order of components in an outputdecision that is produced using an MOR machine learning model can bedetermined simultaneously, the use herein of the subscripts 1, 2, 3, and4 for components and the use of terms herein such as “first component”and “second component” do not denote or imply an order but rather areused herein simply for convenience in distinguishing one component fromanother.

At block 404, each possible value for each output component isinitialized to a predetermined output value. For example, the MORmachine learning module 212 may initialize each possible value for eachof the output components z₁, z₂, z₃, and z₄ to the same output value of1/N, where N is the number of possible values for the output component,so that the sum of the initial output values of possible values for theoutput component is equal to 1. Alternatively, the MOR machine learningmodule 212 may initialize each possible value for each of the outputcomponents z₁, z₂, z₃, and z₄ to another predetermined output valueincluding, but not limited to, an output value based on resourceavailability, based on a baseline, or based on Bayes priors.

In this example, assume that each of the components z₁, z₂, z₃, and z₄has three (3) possible values as follows: z₁ε{z₁₁, z₁₂, z₁₃}={sales vicepresident, sales manager, sales representative}; z₂ε{z₂₁, z₂₂,z₂₃}={call, email, fax}; z₃ε{z₃₁, z₃₂, z₃₃}={MT1, MT2, MT3}; andz₄ε{z₄₁, z₄₂, z₄₃}={short, medium, long}. In this example, for MLP1,MLP2, MLP3, and MLP4, there are total of twelve (12) possible inputvalues z_(ij); where iε{1, 2, 3, 4} and jε{1, 2, 3}. The inputs for theMLP neural network MLP1 are (x, z₂, z₃, z₄). There are total of nine (9)possible values for components z₂, z₃ and z₄, namely, three (3) possiblevalues z₂₁, z₂₂, z₂₃ for z₂, three (3) possible values z₃₁, z₃₂, z₃₃ forz₃, and three (3) possible values z₄₁, z₄₂, z₄₃ for z₄.

The output value of each of the nine (9) possible values for the inputcomponents of the MLP neural network MLP1 may be initialized to ⅓ sinceN=3, namely, a(z₂₁), a(z₂₂), a(z₂₃), a(z₃₁), a(z₃₂), a(z₃₃), a(z₄₁),a(z₄₂), and a(z₄₃) can each be initialized to ⅓, where “a(z_(ij))” is anactivation that represents an output value, iε{1, 2, 3, 4}; and jε{1, 2,3}. In a similar manner, the output value of each of the nine (9)possible values for the input components of each of the MLP neuralnetworks MLP2, MLP3, and MLP4 may also be initialized to ⅓. For example,where z₂ε{z₂₁, z₂₂, z₂₃}={call, email, fax}, a(z₂₁) is the activation ofone of the possible values, namely “call”, and may be initialized toa(z₂₁)(t)=0.33 at iteration number t=1.

Alternatively, the output value of each of the twelve (12) possiblevalues for the input components of each of the MLP neural networks MLP1,MLP2, MLP3, and MLP4 may also be initialized to another identical outputvalue, such as an output value less than ⅓, for example, or tonon-identical output value based on resource availability, based on abaseline, or based on Bayes priors, for example.

At block 406, a relaxation iteration is run on each classifier to updatethe output value of each possible value for each output component. Forexample, the MOR machine learning module 212 may run a relaxationiteration on each of the MLP neural networks MLP1, MLP2, MLP3, and MLP4to update the output value of each possible value for each of the outputcomponents z₁, z₂, z₃, and z₄. In this example, running a relaxationiteration on the MLP neural network MLP1 will generate three (3) outputvalues that are retrieved directly from MLP1, namely p(z₁₁), p(z₁₂), andp(z₁₃). Similarly, running a relaxation iteration on the MLP neuralnetworks MLP2, MLP3, and MLP4 will generate nine (9) total outputvalues, namely p(z₂₁), p(z₂₂), p(z₂₃), p(z₃₁), p(z₃₂), p(z₃₃), p(z₄₁),p(z₄₂), and p(z₄₃). These twelve (12) output values p(z_(ij))(iε{1, 2,3, 4} and jε{1, 2, 3}) may be considered as initial estimate values forthe (12) output values a(z_(ij)), and will be used as learning targetsfor updating output values a(z_(ij)) in the next relaxation iteration,namely iteration number t+1, using the formulaa(z_(ij))(t+1)=a(z_(ij))(t)+R·(p(z_(ij))(t)−a(z_(ij))(t)), which isdiscussed in greater detail below. For example, where iteration numbert=1, a(z₂₁)(t) is updated by running a relaxation iteration on MLP2 toproduce the output value p(z₂₁)(t)=0.47 of z₂₁. In this example,a(z₂₁)(t) is the output value of MLP2 at iteration number t and it isused as an input for MLP1, MLP3, and MLP4 in the next iteration, namelyiteration number t+1. In this example, p(z₂₁)(t) is the output valueretrieved directly from MLP2 at iteration number t and it is used as atarget for updating a(z₂₁)(t+1).

At decision block 408, it is determined whether a relaxation state hasreached an equilibrium. If the relaxation state has reached anequilibrium, (“Yes” at decision block 408), then the method 400 proceedsto block 412. If the relaxation state has not reached an equilibrium(“No” at decision block 408), then the method 400 proceeds to decisionblock 410.

For example, the MOR machine learning module 212 may determine whether arelaxation state has reached an equilibrium. Whether an equilibrium hasbeen reached may be determined according to the following two formulas.First, a relaxation rate is applied to update the output value of eachpossible value for each of the output components z₁, z₂, z₃, and z₄ asfollows: a(z_(ij))(t+1)=a(z_(ij))(t)+R·(p(z_(ij))(t)−a(z_(ij))(t));where R is a relaxation rate; t is the number of iterations; iε{1, 2, 3,4}; and jε{1, 2, 3}. Second, once the following equation is true, therelaxation may be considered to have reached an equilibrium:|a(z_(ij))(t+1)−a(z_(ij))(t)|≦T; where T is a threshold.

For example, where iteration number t=1, and where R=0.1, and using theexample values of a(z₂₁)(t) and p(z₂₁)(t) of z₂₁ from above, namelya(z₂₁)(1)=0.33 and p(z₂₁)(1)=0.47, the formulaa(z_(ij))(t+1)=a(z_(ij))(t)+R·(p(z_(ij))(t)−a(z_(ij))(t) is firstprocessed as follows: a(z₂₁)(2)=0.33+0.1 ·(0.47−0.33)=0.344. Second,where T=0.01, the formula a(z_(ij))(t+1)−a(z_(ij))(t)| is processed asfollows: |0.344−0.33|=0.014. Since 0.014 is greater than 0.01, then thestatement |a(z_(ij))(t+1)−a(z_(ij))(t)|≦T is false, and the relaxationis not considered to have reached an equilibrium.

At decision block 410, it is determined whether a maximum number ofrelaxation iterations has been reached. If the maximum number ofrelaxation iterations has been reached, (“Yes” at decision block 420),then the method 400 proceeds to block 412. If the maximum number ofrelaxation iterations has not been reached (“No” at decision block 410),then the method 400 returns to block 406 for another relaxationiteration.

For example, the MOR machine learning module 212 may determine whether amaximum number of relaxation iterations has been reached. In thisexample, once the following equation is false, the maximum number ofrelaxation iterations may be considered to have been reached: t≦M; wheret is the iteration number, and M is the maximum number of relaxationiterations.

For example, where iteration number t=1, and M=100, since 1 is less than100, then the statement t≦M is true, and the maximum number ofrelaxation iterations is not considered to have been reached. Therefore,the method 400 may return to block 406 for another relaxation iterationwhere iteration number t=2, a(z21)(2)=0.344 is used as an input to MLP1,MLP3, and MLP4. Similarly, MLP2 receives input from correspondinga(z_(ij)) (2) values retrieved from MLP1, MLP3, and MLP4. From theseinputs, MLP2 generates an output value p(z₂₁)(2). The value of a(z₂₁)(3)can then be calculated from (z₂₁)(2) and an output value p(z₂₁)(2) usingthe formula a(z₂₁)(3)=a(z₂₁)(2)+R·(p(z₂₁)(2)−a(z₂₁)(2)). In thisexample, at iteration number t=2, the other eleven (11) a(z_(ij))(3)will also be updated using outputs p(z_(ij))(2) of the appropriateclassifiers and a(z_(ij))(2) at iteration number t=2 as inputs to theformula a(z_(ij))(3)=a(z_(ij))(2)+R·(p(z_(ij))(2)−a(z_(ij))(2)).

At block 412, an optimal output component is retrieved from eachclassifier. For example, the MOR machine learning module 212 mayretrieve an optical output component for each of the components z₁, z₂,z₃, and z₄ from the MLP neural networks MLP1, MLP2, MLP3, and MLP4,respectively.

It is understood that the above-illustrated example is but one exampleof employing an MOR machine learning model to predict multipleinterdependent output components of an MOD output decision, and themethod 400 is not limited to the particular application of this exampleor to the LRM MOD problem solved in this example.

FIG. 5 is a schematic flow chart diagram 500 of multiple correct MODoutput decisions. As disclosed in the diagram 500, the MOR machinelearning model 300 may generate multiple correct output decisions 502and 504 for a given input feature vector x. Although in a typicaldecision making process it is usually assumed that there is a uniquecorrect decision given a fixed input, for LRM MOD decisions there may bemultiple correct decisions which may all produce similar favorableresults. A decision may be chosen among multiple correct decisions basedon available resources. For example, if a particular response agent withresponse agent title z₁=“sales manager” is not available at a particulartime, then another correct decision with response agent title z₁=“salesrepresentative” may be made. Where multiple output decisions aresimultaneously considered to be correct, the term “correct” may refer tomultiple output decisions each having a substantially similar outputvalue. For example, each of the output decisions 502 and 504 of FIG. 5may have an identical or substantially similar output value, whichindicates that performing either output decision would produce similarfavorable results. Additionally or alternatively, the term “correct” mayrefer to multiple output decisions each having an output value above apredetermined threshold. The threshold may be predetermined to berelatively high or relatively low, depending on the application.Although only two correct output decisions are disclosed in FIG. 5, itis understood that the MOR machine learning model 300 may generate morethan two correct output decisions.

Having described example methods of employing an MOR machine learningmodel to predict multiple interdependent output components of an MODoutput decision with respect to FIGS. 3A-5, example systems and userinterfaces that enable agents to access and implement the resultingoutput decisions will be described with respect to FIGS. 6-8B. It isunderstood that these specific systems and user interfaces are only someof countless systems and user interfaces in which example embodimentsmay be employed. The scope of the example embodiments is not intended tobe limited to any particular system or user interface.

FIG. 6 illustrates an example computer screen image of a user interface600 of an example customer relationship management (CRM) system. Theuser interface 600 includes various controls that allow an agent tomanage customer relationships and, in particular, manage leads that areprovided by the CRM system. The user interface 600 may be presented toan agent by the web server 170 on the workstations 128 or on the localagent workstations 192 of FIG. 1, for example. The agent may use theuser interface 600 to respond to leads that have been previously storedon the lead data server 190 of FIG. 1. In particular, the lead advisordisplay 800 may allow the agent to respond to leads in a manner thatoptimizes contact or qualification rates, as discussed below inconnection with FIGS. 8A and 8B.

FIG. 7 illustrates an example computer screen image of a user interface700 of an example LRM system, such as the LRM system of FIG. 1. Like theuser interface 600 of FIG. 6, the user interface 700 includes variouscontrols that allow an agent to respond to lead. The user interface 700may be presented to an agent in a similar manner as the user interface600. The user interface also includes a lead advisor display 800.

FIG. 8A illustrates an example computer screen image of the example leadadvisor display 800 before a lead has been selected by an agent and FIG.8B illustrates an example computer screen image of the example leadadvisor display 800 after a lead has been selected by an agent. Asdisclosed in FIG. 8A, the lead advisor display 800 lists five leads.Each lead includes a name 802, a likelihood of success meter 804, and alikelihood of success category indicator 806. As disclosed in FIG. 8A,the leads are listed by highest likelihood of success to lowestlikelihood of success. Upon inquiry by the agent, by mousing-over a leadwith a mouse pointer for example, the lead may expand as shown in FIG.8A for lead “Mark Littlefield.” Upon expansion, the lead may present theagent with additional options, such as a confirm button 808, a deletebutton 810, and a “more info” link 812.

Upon selection of the “more info” link 812 by the agent, by clicking onthe more info link 812 with a mouse pointer for example, the agent maybe presented with a pop-out display 814 as disclosed in FIG. 8B. Thepop-out display 814 may present the agent with an LRM plan associatedwith the lead. This LRM plan may have been generated by the examplemethods disclosed herein and may reflect the output decision with thehighest, or among the highest, output value for the lead. As disclosedin FIG. 8B, the LRM plan for the lead named “Mark Littlefield” mayinclude employing a sales manager to send an email with message type MT1in a short timeframe, which corresponds to the output decision 502 ofFIG. 5. The agent may then simply click on the pop-out display 814 tohave the lead advisor display 800 automatically generate an email to thelead with message type MT1 that will be sent by a sales managerimmediately. Alternatively, the agent may manually override the responseplan and manually perform a different response.

Therefore, the embodiments disclosed herein include methods of employingan MOR machine learning model to predict multiple interdependent outputcomponents of an MOD output decision. The example methods disclosedherein enable the prediction of each output component based on an inputand based on all of the other output components. Therefore, the examplemethods disclosed herein may be employed to solve MOD problems such asLRM problems.

The embodiments described herein may include the use of a specialpurpose or general-purpose computer including various computer hardwareor software modules, as discussed in greater detail below.

Embodiments described herein may be implemented using computer-readablemedia for carrying or having computer-executable instructions or datastructures stored thereon. Such computer-readable media may be anyavailable media that may be accessed by a general purpose or specialpurpose computer. By way of example, and not limitation, suchcomputer-readable media may include non-transitory computer-readablestorage media including RAM, ROM, EEPROM, CD-ROM or other optical diskstorage, magnetic disk storage or other magnetic storage devices, or anyother storage medium which may be used to carry or store desired programcode in the form of computer-executable instructions or data structuresand which may be accessed by a general purpose or special purposecomputer. Combinations of the above may also be included within thescope of computer-readable media.

Computer-executable instructions comprise, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. Although the subject matter has been described inlanguage specific to structural features and/or methodological acts, itis to be understood that the subject matter defined in the appendedclaims is not necessarily limited to the specific features or actsdescribed above. Rather, the specific features and acts described aboveare disclosed as example forms of implementing the claims.

As used herein, the term “module” may refer to software objects orroutines that execute on the computing system. The different modulesdescribed herein may be implemented as objects or processes that executeon the computing system (e.g., as separate threads). While the systemand methods described herein are preferably implemented in software,implementations in hardware or a combination of software and hardwareare also possible and contemplated.

All examples and conditional language recited herein are intended forpedagogical objects to aid the reader in understanding the exampleembodiments and the concepts contributed by the inventor to furtheringthe art, and are to be construed as being without limitation to suchspecifically recited examples and conditions.

What is claimed is:
 1. A method for employing a multiple outputrelaxation (MOR) machine learning model to predict multipleinterdependent output components of a multiple output dependency (MOD)output decision, the method comprising: training a classifier for eachof multiple interdependent output components of an MOD output decisionto predict the component based on an input and based on all of the othercomponents; initializing each possible value for each of the componentsto a predetermined output value; running relaxation iterations on eachof the classifiers to update the output value of each possible value foreach of the components until a relaxation state reaches an equilibriumor a maximum number of relaxation iterations is reached; and retrievingan optimal component from each of the classifiers.